System and method for failure detection for artificial lift systems

ABSTRACT

A computer-implemented artificial lift detection system, method, and software are provided for failure detection for artificial lift systems, such as sucker rod pump systems. The method includes providing artificial lift system data from an artificial lift system. Attributes are extracted from the artificial lift system data. Data mining techniques are applied to the attributes to determine whether the artificial lift system is detected to fail within a given time period. An alert is output indicative of impending artificial lift system failures.

TECHNICAL FIELD

This invention relates to artificial lift system failures in oil fieldassets, and more particularly, to a system, method, and computer programproduct for failure detection for artificial lift systems.

BACKGROUND

Artificial lift techniques are widely used to enhance production forreservoirs with formation pressure too low to provide enough energy todirectly lift fluids to the surface. Among various artificial lifttechniques in the industry (such as Gas Lift, Hydraulic Pumping Units,Electric Submersible Pump, Progressive Cavity Pump, Plunger Lifts andRod Pump techniques), the Sucker Rod Pump technique is the most commonlyused artificial lift method. For example, rod pump systems currentlyconstitute approximately 59% of all Artificial Lift in North America and71% in the rest of the world. Furthermore, about 80% of United Statesoil wells are considered to be marginal or stripper wells, which producean average of ten barrels per day or less over a twelve month period andprimarily are produced using rod pump systems. In the United States, rodpump systems are currently used on about 350,000 wells.

There are many types of failures for rod pump systems including tubingfailures, rod string failures and rod pump failures. The reasons for rodpump system failures can be broadly classified into two main categories:mechanical and chemical. Mechanical failures are caused by improperdesign, by improper manufacturing, or by wear and tear duringoperations. Well conditions such as sand intrusions, gas pounding, andasphalting can contribute to excessive wear and tear. Chemical failuresare generally caused by the corrosive nature of the fluid being pumpedthrough the systems. For example, the fluid may contain hydrogen sulfide(H₂S) or bacteria that excrete corrosive chemicals. Mechanical andchemical failures initially reduce the efficiency of pumping operations,but in due course will bring the systems to fail, thus requiringreactive well work. Wells are shut down to perform workovers, whichresults in production loss and an increase in the operating expenditure(OPEX) in addition to the regular maintenance cost.

Currently, pump off controllers (POCs) play a significant role inmonitoring and controlling the operation of rod pump systems. Forexample, the POCs can be programmed to automatically shut down units ifthe values of torque and load deviate beyond a torque/load threshold.While POCs reduce the amount of work required by the production andmaintenance personnel operating the field, they may not be sufficientsince a great deal of time and effort is still needed to monitor eachand every operating unit. The dynamometer card patterns collected by thePOCs can be analyzed to better understand the behavior of the rod pumpsystems. However, successful analysis is directly linked to the skilland experience of the analyst and even the most knowledgable analystscan be misled into an incorrect diagnosis. In some cases, thedynamometer card may miss some early warnings of rod pump systemfailures. Furthermore, the well measurement dataset obtained by POCsoften poses difficult challenges to data mining with respect to highdimensionality, noise, and inadequate labeling.

The data collected from POCs is inherently highly dimensional as POCcontrollers gather and record periodic well sensor measurementsindicating production and well status through load cells, motor sensors,pressure transducers and relays. For example, in a dataset having 14attributes where each attribute is measured daily, the dimension is 1400for a dataset over a hundred day period.

Datasets for well measurement artificial lift also tend to be verynoisy. The noise is produced from multiple sources, which includenatural and manmade causes. The wells operate in rough physicalenvironments which often results in equipment break down. For example,lightning strikes can sometimes disrupt wireless communication networks.Data collected by the POC sensors is therefore not received by acentralized logging database, which results in missing values in thedata. Additionally, petroleum engineering field workers regularlyperform maintenance and make calibration adjustments to the equipment.These maintenance activities and adjustments can cause the sensormeasurements to change—sometimes considerably. For example, the POCsensors are occasionally recalibrated, which can introduce extremechanges in sensor readings. It is not standard practice to record suchrecalibrations. Furthermore, while workers are generally diligent withregards to logging their work in downtime and workover database tables,occasionally a log entry is delayed or not logged at all. Another sourceof data noise is the variation caused by the force drive mechanisms. Inoil fields with insufficient formation pressure, injection wells can beused to inject fluid (e.g., water, steam, polymer, carbon dioxide) intothe reservoir to drive hydrocarbons toward production wells. This fluidinjection can also affect the POC sensors measurements.

The datasets received by POCs are also not explicitly labeled. Manuallylabeling the dataset received by a POC is generally too time consumingand very tedious. Furthermore, access to petroleum engineering subjectmatter experts (SMEs) to perform the manual labeling is also oftenlimited. Fully automatic labeling is also problematic. Although the wellfailure events are recorded in the well database, they are not suitablefor direct use because of semantic differences in the interpretation ofwell failure dates. In general, the well failure dates in the databasedo not correspond to the actual failure dates, or even to the dates whenthe SMEs first notice the failures. Rather, the recorded failure datestypically correspond to the date when the workers shut down the well tobegin repairs. Because of the backlog of well repair jobs, thedifference can be several months between the actual failure dates andthe recorded failure dates. Moreover, even if the exact failure datesare known, differentiation of the failures among normal, pre-failure andfailure signals still needs to be performed.

FIG. 1 shows an example of a past well failure where several selectedattributes collected through a POC are displayed. As shown in FIG. 1,the well's failure was detected by field personnel on Mar. 31, 2010.After pulling all the pumping systems above the ground, they discoveredthat there were holes on the tubing that were causing the leakingproblems, which in turn, reduced the fluid load the rod pump carried tothe surface. Through a “look back” process, it was found that the actualleak started around Feb. 24, 2010. Even before that, a subject matterexpert established that “rod cut” events likely started around Nov. 25,2009, wherein the rod began cutting the tubing. After the initialcutting, the problem continually grew worse cutting larger holes intothe tubing.

The inventors therefore have recognized a need for more automatedsystems, such as artificial intelligent systems that can dynamicallykeep track of certain parameters in a group of artificial lift systems,detect impending system failures and provide early indications orwarnings thereof, and provide suggestions on types of maintenance workto address the detected failures including providing an optimal workschedule for performing such work. Such systems would be a great assetto industry personnel by potentially allowing them to be more proactiveand to make better maintenance decisions. These systems could increasethe efficiency of the artificial lift systems to bring down OperatingExpenditure (OPEX), thereby making the artificial lift operations moreeconomical.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example well failure and failure pattern.

FIG. 2 shows early detection of true failures in the oilfield (2A) andearly detection of pre-signals of true failures in the oilfield (2B),according to embodiments of the present invention.

FIG. 3 is a flow diagram for detecting artificial lift system failures,according to an embodiment of the present invention.

FIGS. 4A and 4B show the results of using data preparation techniques,according to an embodiment of the present invention.

FIG. 5 is a flow diagram for training and testing a stochastic learningmodel, according to an embodiment of the present invention.

FIG. 6 shows an algorithm for detecting artificial lift system failures,according to an embodiment of the present invention.

FIG. 7 shows training and testing results for detecting artificial liftsystem failures, according to an embodiment of the present invention.

FIG. 8 shows pre-failure and failure signal testing results for anartificial lift system having a tubing failure, according to anembodiment of the present invention.

FIG. 9 shows pre-failure and failure signal testing results for anartificial lift system having a rod failure, according to an embodimentof the present invention.

FIG. 10 shows pre-failure signal testing results for a normal artificiallift system, according to an embodiment of the present invention.

FIG. 11 shows a system for detecting failures in artificial liftsystems, according to an embodiment of the present invention.

DETAILED DESCRIPTION

Embodiments of the present invention relate to artificial lift systemfailures in oil field assets, which lead to production loss and cangreatly increase operational expenditures. In particular, systems,methods, and computer program products are disclosed for analyzing anddetecting the performance of artificial lift systems such as sucker rodpumps. For example, these units are developed using artificialintelligence (AI) and data mining techniques. Detecting artificial liftsystem failures can dramatically improve performance, such as byadjusting operating parameters to forestall failures or by schedulingmaintenance to reduce unplanned repairs and minimize downtime.

As will be described in more detail herein, an automatic early failuredetection framework and corresponding algorithms for artificial liftsystems are disclosed. State-of-the-art data mining approaches areadapted to learn patterns of dynamical pre-failure and normal well timeseries records, which are used to make failure detections. Measured datafrom artificial lift systems can include many parameters such as cardarea, production, daily run time, stoke per minutes, and otherparameters. For example, in one embodiment, more than 14 parameters offunctioning of a sucker rod pump system are considered in data mining.Two classification based detection models are employed by combining twosupervised learning algorithms. The models are calibrated to learn andeasily recogonize any anomaly, failure, pre-failure, and normal patternfrom archival historic pump operational data.

For convenience and simplicity, rod pumps are used to illustrate theearly failure detection using the models. The results show that thefailure detection framework is capable of capturing future rod pump andtubing failures using data from real-world assets using rod pumpartificial lift systems. The models use measured data to learn andautomatically detect possible failures in a large asset of thousands ofwells with over 90% accuracy. Management of detected failures can beprioritized for production engineer for timely and suitable actions. Themethods can enable engineers detect problems in the artificial liftoperations before they happen from a remote operations control center.Based on detection results, a corresponding action plan can beimplemented to minimize losses in production and re-entry cost. Fieldexperts can not only remotely recognize the problem before any on-siteaction, but also save significant time and labor cost.

FIG. 2 shows that the constructed models are capable detecting thepossible failures in the field, as well as, providing early detection ofemerging failures. In particular, FIG. 2A shows early detection of truefailures in the oilfield and FIG. 2B shows early detection ofpre-signals of true failures in the oilfield. It is assumed that thereare several pre-failure signals before failures happen (PS1, PS2 andPre-failure Signal). Furthermore, there is usually a time lag betweenwhen a true hazard or failure occurs and when field engineers recognizethe occurrence of failures, which is typically due to human delay.Recorded failures are shown in the timeline of FIG. 2 as “Rec. Failure”.The models can accelerate early recognition of failures in the oilfieldand at a much earlier time than what can be done by the engineers.

FIG. 3 shows a workflow for detection of failures in artificial liftsystems. The workflow in FIG. 3 can be divided into four maincategories: Data Extraction (which is associated with Step 101), DataPreparation (which is associated with Step 103), Data Mining (which isassociated with Steps 105, 107), and Outputs/Alerts (which is associatedwith Step 109). As will be described, Data Extraction comprisesretrieving data from one or more databases or information storage systemsuch as a System of Records (SOR), DataMart™, LOWIS™ (Life of WellInformation Software), or Microsoft® Access. Data (e.g. workovers, beamanalysis tables, etc) needed for failure detection can be extracted intotables, graphs, equations, or other usable format. Data Preparationtypically includes preparing and filtering data. Data preparation canaddress the problem of noise and missing values. Data Mining includestransforming the raw data, which is extracted from a daily surveillancerecord, into features and training and testing of ArtificialIntelligence (AI) Models to detect failures in the oilfield. Forexample, general supervised learning algorithms can be applied to train,test and evaluate results in Data Mining. The Outputs/Alerts stage isused to communicate detected failures to work personnel such as througha visualized interface.

Data Extraction

To perform failure detection, data is first collected for artificiallift systems of interest, such as sucker rod pumps. For example, datacan be collected from pump off controllers (POCs), which gather andrecord periodic well sensor measurements indicating production and wellstatus through load cells, motor sensors, pressure transducers andrelays. Some attributes recorded by these sensors are card area, peaksurface load, minimum surface load, strokes per minute, surface strokelength, flow line pressure, pump fillage, yesterday cycles, and dailyrun time, while calculated GB torque, polished rod HP, and net DH pumpefficiency are calculated. These attributes are typically measureddaily, sent over wireless network, and recorded in a database such asthose available under the trade names of LOWIS™ (Life of WellInformation Software) or DataMart™, which are both available fromWeatherford International Ltd. Another example of a database or storagesystem (System of Records) is Microsoft® Access. In the database,attribute values can be indexed by a well identifier and a date. Inaddition to these daily measurements, field specialists can performintermittent tests and enter the test results into the database. Theseattributes include last approved oil, last approved water, and fluidlevel. Since these attributes are generally not measured daily, themissing daily values can be automatically filled in with the previousmeasurement, i.e., these attribute values are assumed to be piecewiseconstants. Finally, a special attribute called “class” can be added thatindicates the daily status of the well, i.e. either it is performingnormally, is in pre-failure stage, or it has failed. All theseattributes together define a labeled multivariate time series datasetfor artificial lift systems.

The attributes can be partitioned into a plurality of attribute groupsand ranked according to a metric that combines relevancy to failurepredication and data quality. In one embodiment, the attribute groupsare divided into three groups, which are labeled A, B and C with group Abeing the most relevant and having the highest data quality.

-   -   A. Card area, peak surface load, minimum surface load, yesterday        cycles, daily run time    -   B. Strokes per minute, pump fillage, calculated GB torque,        polished rod HP, net DH pump efficiency, gross fluid rate (sum        of last approved oil and water), flow line pressure    -   C. Surface stroke length

Software connectors are used to extract data in Step 101 from theartificial lift databases and feed it to the prediction system. Forexample, this can be achieved by running a SQL query on the database,such as LOWIS™ or the DataMart™, to extract the necessary attributes foreach well in the form of time series.

Data Preparation

Raw artificial lift time series data typically contains noise andfaults, which can be attributed to multiple factors. For example, severeweather conditions, such as lighting strikes, can disrupt communicationcausing data to be dropped. Transcription errors may occur if data ismanually entered into the system. This noisy and faulty data cansignificantly degrade the performance of data mining algorithms. Datapreparation is performed in Step 103 to reduce the noise as much aspossible. An example of a noise reduction technique includes using theGrubbs's test to detect outliers and applying a locally weighted scatterplot smoothing algorithm to smooth the impact of the outliers. Thedenoised data is then transformed into features and all the time seriesdata is transformed into a feature set.

FIG. 4 shows the impact of outliers and the results for before (4A) andafter (4B) the smoothing process using linear regression on artificialdata points where random Gaussian noise and two outliers were added. Thetwo outliers biased the curve by introducing two local peaks—which infact do not exist. After the effects of the outliers were removed byusing the smoothing process, the same regression algorithm is able torecover the original shape of the curve.

Data Mining

Each artificial lift system is characterized by multiple attributes,where each attribute by itself is a temporal sequence. This type ofdataset is called a multivariate time series, where each multivariatetime series refers to the data for a specific well. As used herein, amultivariate time series T=t₁, t₂, . . . , t_(m) comprises an orderedset of m variables. Each variable t_(i) is a k-tuple, where each tuplet_(i)=t_(i1), t_(i2), t_(i3), . . . , t_(ik) contains k real-values. Theraw data, which is extracted from a daily surveillance record, istransformed into features in Step 105. For example, methods that can beused for feature extraction include those described by Li Wei and EamonnKeogh at the 12th ACM SIGKDD international conference on knowledgediscovery and data mining (Li Wei, Eamonn J. Keogh: Semi-supervised timeseries classification. KDD 2006: 748-753), which is herein incorporatedby reference in its entirety.

The training dataset typically includes data from many artificial liftsystems from the same field. In some embodiments, the training datasetcontains data from artificial lift systems across multiple fields. Allthe wells are different in terms of rod pump system and well conditions.All of the well data is normalized in order to eliminate thesedifferences. Normalization is based on the global median. As usedherein, the percentage change is used instead of the absolute value,which is obtained by dividing the absolute value by the mean value. Thispre-processing shows good results. Multi-dimensional scaling assistedlabeling is used such that the multi-dimensional data is plotted into a2D plot, and the difference between pre-failure, failure, and normalpoints can be visually determined.

FIG. 5 shows a workflow for training and testing of ArtificialIntelligence (AI) Models according to Step 107 in FIG. 3. For example,supervised learning algorithms can be applied to train, test andevaluate results in Data Mining. In supervised learning, the data miningalgorithm is given positive and negative training examples of theconcept the algorithm is supposed to learn. The formulation for thisartificial lift domain is given an artificial lift multivariate timeseries with well class labels as the training dataset, which can be usedto generate a failure detection model. When given previously unseenwells from a test dataset with their multivariate time series, but notthe class values, the model can predict class values for that well. Thislearning problem is called supervised, because the class labels are usedto direct the learning behavior of the data mining algorithm. Insupervised learning, the resulting detection model does not change withrespect to well data from the testing set.

Examples of supervised learning include AdaBoost Bayesian Network(AdaBNet) machine learning algorithms and AdaBoost Decision Tree (AdaDT)machine learning algorithms. FIG. 6 shows the AdaBNet algorithm.AdaBoost is an adaptive boosting algorithm. Unlike other algorithms thatlearn only one algorithm, AdaBoost is an algorithm for constructing a“strong” classifier as linear combination of “simple weak” classifiersh_(t)(x).

${f(x)} = {\sum\limits_{t = 1}^{T}\; {\alpha_{t}{h_{t}(x)}}}$

where h_(t)(x) is weak or basis classifier, and α_(t) is the weight ofthis classifier at any given time, t.

The final output of AdaBoost is H(x)=sign(f(x)). AdaBoost utilizes theweighted training set. During learning of each basic classifier,h_(t)(x), it increases weight of misclassified examples and decreasesweight of correctly classified examples. AdaBoost is simple to implementand can achieve a very low training error.

AdaBoost can be viewed as optimizing the following exponential lossfunction,

L _(exp)(x,y)=e ^(−yf(x))

${f(x)} = {\frac{1}{2}{\sum\limits_{t = 1}^{T}\; {\alpha_{t}{h_{t}(x)}}}}$

(the factor of ½ has no effect on the classifier output) is rewritten,then the full learning objective function is

$\min \left( {\sum\limits_{i}^{\;}\; ^{{- \frac{1}{2}}y_{i}{\sum\limits_{t = 1}^{T}\; {\alpha_{t}{h_{t}{(x)}}}}}} \right)$

which is optimized with respect to the weights α_(t) and the parametersof the basic classifiers. Note that the exponential loss is anupper-bound on the 0-1 loss,

L _(exp)(x,y)≧L ₀₋₁(x,y)

The basic classifiers are restricted as either Bayesian networks ordecision trees, which are called AdaBNet and AdaDT algorithm,respectively. The optimal choice for h_(t)(x) is the weak classifierthat minimizes the weighted number of errors,

h _(t)=argmin_(h)Σ_(i=1) ^(N) D _(t)(i)I[y _(i) ≠h(x _(i))]

where D_(t)(i) is a distribution of weights over the training set, and Nis the size of the training set. The minimum fraction of errors for thatclassifier is

ε_(t)=Σ_(i=1) ^(N) D _(t)(i)I[y _(i) ≠h(x _(i))]

Error Bound of AdaBNet and AdaDT

The training error of the AdaBoost algorithm is:

${P_{T}({error})} \leq {\sum\limits_{t = 1}^{T}\; \left\lbrack {2\sqrt{ɛ_{t}\left( {1 - ɛ_{t}} \right)}} \right\rbrack}$

If we let

${ɛ_{t} = {\frac{1}{2} - \gamma_{t}}},$

then

${{P_{T}({error})} \leq {\prod\limits_{t = 1}^{T}\mspace{11mu} \left\lbrack {2\sqrt{ɛ_{t}\left( {1 - ɛ_{t}} \right)}} \right\rbrack}} = {{\prod\limits_{t = 1}^{T}\mspace{11mu} \sqrt{1 - {4\gamma_{t}^{2}}}} = {{\exp \left( {- {\sum\limits_{t = 1}^{T}{{KL}\left( {{{1/2}{}{1/2}} - \gamma_{t}} \right)}}} \right)} \leq {\exp \left( {{- 2}{\sum\limits_{t = 1}^{T}\gamma_{t}^{2}}} \right)}}}$

where KL(a∥b)=aln(a/b)+(1−a)ln((1−a)/(1−b)) is the Kullback-Leiblerdivergence.

This means that the training error drops exponentially if each weakclassifier is slightly better than random. Also, if the errors of allthe classifiers are

${ɛ_{t} = {\frac{1}{2} - \gamma}},$

then P_(T)(error)≦exp(−2Tγ²). That is to say, as the number ofiterations goes towards infinity, the upper bound of the finalclassifier error approaches zero. Accordingly, AdaBoost can convert aweak learning algorithm into a strong learning algorithm, witharbitrarily low error rates.

Overfitting

Overfitting generally occurs when a model is excessively complex or toopowerful in relation to the amount of data available. When a model isoverfitted, it will lose accuracy during detection procedure. With theexponential loss and 0-1 loss, there is no overfitting for AdaBNet andAdaDT algorithms. Using the objective function to minimize

$E = {\sum\limits_{i}^{\;}\; ^{{- \frac{1}{2}}y_{i}{\sum\limits_{t = 1}^{T}\; {\alpha_{t}{h_{t}{(x)}}}}}}$

Consider the weak classifier f_(t) to be added at step t, then,

$E = {\sum\limits_{i}^{\;}\; ^{{{- \frac{1}{2}}y_{i}{\sum\limits_{j = 1}^{t - 1}\; {\alpha_{j}{h_{j}{(x)}}}}} - {\frac{1}{2}y_{i}\alpha_{t}{h_{t}{(x)}}}}}$$E = {\sum\limits_{i}^{\;}\; {{D_{t}(i)}^{{- \frac{1}{2}}y_{i}\alpha_{t}{h_{t}{(x)}}}}}$$E = {{\sum\limits_{{i\text{:}\mspace{14mu} {h_{t}{(x_{i})}}} = y_{i}}^{\;}\; {{D_{t}(i)}^{{- \alpha_{t}}/2}}} + {\sum\limits_{{i\text{:}\mspace{14mu} {h_{t}{(x_{i})}}} \neq y_{i}}^{\;}\; {{D_{t}(i)}^{\alpha_{t}/2}}}}$$E = {{\left( {^{\alpha_{t}/2} - ^{{- \alpha_{t}}/2}} \right){\sum\limits_{i}^{\;}{{D_{t}(i)}{I\left( {{h_{t}\left( x_{i} \right)} \neq y_{i}} \right)}}}} + {^{{- \alpha_{t}}/2}{\sum\limits_{i}^{\;}{D_{t}(i)}}}}$

where

${D_{t}(i)} = \; {^{{- \frac{1}{2}}y_{i}{\sum\limits_{j = 1}^{t - 1}\; {\alpha_{j}{h_{j}{(x)}}}}}.}$

The optimal value for α_(t) can be solved as,

$\frac{E}{\alpha_{t}} = {{{\frac{1}{2}\left( {^{\alpha_{t}/2} + ^{{- \alpha_{t}}/2}} \right){\sum\limits_{i}^{\;}{D_{t}(i){I\left( {{h_{t}\left( x_{i} \right)} \neq y_{i}} \right)}}}} - {\frac{1}{2}^{{- \alpha_{t}}/2}{\sum\limits_{i}^{\;}{D_{t}(i)}}}} = 0}$

The following weights are given as,

0 = ^(α_(t)/2)ɛ_(t) + ^(−α_(t)/2)ɛ_(t) − ^(−α_(t)/2)^(α_(t)/2)ɛ_(t) = ^(−α_(t)/2)(1 − ɛ_(t))${\frac{\alpha_{t}}{2} + ɛ_{t}} = {{- \frac{\alpha_{t}}{2}}\left( {1 - ɛ_{t}} \right)}$$ɛ_{t} = {\ln \frac{1 - ɛ_{t}}{ɛ_{t}}}$

This process shows that weights can be found for the algorithms in anysituation and therefore, there is no overfitting in these algorithms.

Output/Alerts

All abnormal events are considered as failures, including true failuresand downtimes. Therefore, in order to verify detection alerts, a lookforward-backward process is used. Alerts, which are communicated to anoperator or technician in Step 109, indicate that an artificial liftsystem is detected to fail. The alert can be utilized to minimizedowntime of the well or for other reservoir management decisions.

EXAMPLES

The below examples focus on tubing failure and rod failure of sucker rodpump artificial lift systems to show the accuracy achieved using AdaBNetand AdaDT algorithms. Data is extracted and labelled as either normal orfailure (i.e., rod failure, tubing failure, and pre-signals of thefailures). This is used as training set to train the models. The trainedmodels are then used to detect possible failures of rod pump systems.The tables below show confusion matrices of training results with10-fold cross validation from both AdaBNet and AdaDT algorithms, whichshow how well the models could be trained. The rows are ground truthlabels and the columns are classified labels by the models. The resultsshow that the training errors for both algorithms are very low (lessthan 0.12%). The Confusion Matrix for AdaBNet, 10-fold cross validation(record level) is below:

Tubing Rod Pre- Error Normal Failure Failure Signal Rate (%) Normal 28981 6 1 0.0027 Tubing Failure 2 18 0 0 0.10 Rod Failure 2 0 46 0 0.042StartToFail 3 0 0 23 0.115The Confusion Matrix for AdaDT, 10-fold cross validation (record level)is below:

Tubing Rod Pre- Error Normal Failure Failure Signal Rate (%) Normal 29031 2 0 0.001 Tubing Failure 0 20 0 0 0 Rod Failure 0 0 48 0 0 StartToFail2 0 0 24 0.076

The AdaBNet model was tested on 426 unknown wells (37 true failure wellsand 389 true normal wells). As shown in the below table, the testingresults indicate the high testing accuracy for both normal wells (97.7%)and failure wells (91.9%) with normalization.

True Normal True Failure Predicted Normal 380 (97.7%) 9 (2.3%) PredictedFailure  3 (8.1%) 34 (91.9%)

FIG. 7 gives the testing results by AdaDT algorithm and shows thehighest test accuracy of AdaDT when number of iteration is between 80and 180, which is about 95%. When number of iteration is more than 180,the model shows overfitting with testing accuracy decreasing.

FIG. 8 shows pre-failure and failure signal testing results for anartificial lift system having a tubing failure. As shown in FIG. 8, thealgorithm successfully detects the tubing failure about 2 months beforethe recorded failure date in the SOR, and pre-tubing failure about 8months ahead of the recorded date in the SOR.

FIG. 9 shows pre-failure and failure signal testing results for anartificial lift system having a rod failure. As shown in FIG. 9, thealgorithms can detect the rod pump failures ahead of time.

In FIGS. 8 and 9, there is time delay between true failure date andrecorded failure date because it will take some time for the fieldengineer to recognize and record the failure in the database. Thealgorithms can detect failures ahead of time which can save lots of costand production for the operator. The detection algorithms alsosuccessfully detect all the missing data. Even though it is a trivialjob for one to recognize missing records for a few wells, it is not easywhen we are considering thousands of wells. The detection algorithmsprovide an automatic way to recognize the missing records and leave itto SME about how to interpret the meaning of the missing data, which maybe due to maintenance, failure or other reasons.

FIG. 10 shows pre-failure and failure signal testing results for anormal artificial lift system. The algorithms do not give false alarmson the selected well.

The data mining framework is able to automatically recognize earlyfailure of artificial lift systems. Failure detection in rod pump wellsuse parameters that indicate the daily functions of rod pump wells andemploy advanced machine learning techniques. In some embodiments, morethan 14 parameters that indicate the daily functions of rod pump wellsare utilized. The system recognizes failing, failed, and normalsituations by learning patterns/signature from historical pump data,that include card area, peak-surface load, minimum-surface load, dailyrun-time, and production data. This data is automatically pre-processedusing expert domain knowledge to reduce noise and to fill-in missingdata. AdaBoost algorithms, such as AdaBNet and AdaDT, are used forlearning and recognizing possible failures in oilfields.

The machine learning algorithm AdaBNet uses boosting to learn severalBayesian Network models and then combines these models with differentweights to form a stronger boosted model. The approach generates thissingle boosted model that is applicable across all the wells in a field,as opposed to well-specific approaches that generate one model per well.This model detects anomalies, pre-failure and failure signals andgenerates corresponding alerts. Early fault detection in artificial liftsystems, such as rod pump wells, is useful for automatic monitoring oflarge number of assets remotely. A training dataset of 12 wells is usedto construct the learning model for the AdaBNet algorithm. The algorithmis tested on 426 wells from the same field. The testing results showthat the suggested algorithms have high accuracy for early failuredetection, which is more than 90% for the given data.

This framework can help field operators not only to remotely recognizeand predict failures in advance, but also to help prioritize theavailable manpower, save significant time, reduce operating expense(OPEX), downtime and lost production. Early fault detection in rod pumpsystems can allow for proactive maintenance that can delay and evenprevent future well failures. The proposed algorithm can enableproduction engineers remotely detect failures and anomalies before theyoccur, and assess the situation at control centers before taking anyremedial or corrective actions. This approach to using a single modelfor an entire field is superior to other approaches that use anindividual model for each well.

The above described methods can be implemented in the general context ofinstructions executed by a computer. Such computer-executableinstructions may include programs, routines, objects, components, datastructures, and computer software technologies that can be used toperform particular tasks and process abstract data types. Softwareimplementations of the above described methods may be coded in differentlanguages for application in a variety of computing platforms andenvironments. It will be appreciated that the scope and underlyingprinciples of the above described methods are not limited to anyparticular computer software technology.

Moreover, those skilled in the art will appreciate that the abovedescribed methods may be practiced using a processor that comprises anyone or a combination of computer processing system configurations,including, but not limited to, single and multi-processor systems,hand-held devices, programmable consumer electronics, mini-computers, ormainframe computers. The above described methods may also be practicedin distributed computing environments where tasks are performed byservers or other processing devices that are linked through a one ormore data communications networks. In a distributed computingenvironment, program modules may be located in both local and remotecomputer storage media including memory storage devices.

Also, an article of manufacture for use with a computer processor, suchas a CD, pre-recorded disk or other equivalent devices, could include acomputer program storage medium and program means recorded thereon fordirecting the computer processor to facilitate the implementation andpractice of the above described methods. Such devices and articles ofmanufacture also fall within the spirit and scope of the presentinvention.

As will be described, the invention can be implemented in numerous ways,including for example as a method (including a computer-implementedmethod), a system (including a computer processing system), anapparatus, a computer readable medium, a computer program product, agraphical user interface, a web portal, or a data structure tangiblyfixed in a computer readable memory.

FIG. 11 illustrates a system 200 for analyzing and detecting failuresfor artificial lift systems such as on sucker rod pumps, such as byusing method 100. System 200 includes user interface 210, such that anoperator can actively input information and review operations of system200. User interface 210 can be any means in which a person is capable ofinteracting with system 200 such as a keyboard, mouse, or touch-screendisplay. Operator-entered data input into system 200 through userinterface 210, can be stored in database 230. Measured well data such asfrom POCs, which is received by one or more well sensors 220, can alsobe input into system 200 for storage in database 230. Additionally, anyinformation generated by system 200 can be stored in database 230.

Database 230 can store, for example, well sensor measurements 231indicating artificial lift information through load cells, motorsensors, pressure transducers and relays. Some attributes recorded bywell sensors 220 are card area, peak surface load, minimum surface load,strokes per minute, surface stroke length, flow line pressure, pumpfillage, yesterday cycles, and daily run time. Furthermore, GB torque,polished rod HP, and net DH pump efficiency can be calculated forstorage in database 230. Well test data 233 can also be stored indatabase 230, which can include last approved oil, last approved water,and fluid level.

System 200 includes a computer program product or software 240 that isstored on a processor readable medium. Current examples of a processorreadable medium include, but are not limited to, an electronic circuit,a semiconductor memory device, a ROM, a flash memory, an erasableprogrammable ROM (EPROM), a floppy diskette, a compact disk (CD-ROM), anoptical disk, a hard disk, a fiber optic medium and/or other tangiblemedia. As will be described more fully herein, software 240 can includea plurality of modules for performing system tasks such as performingthe methods, such as method 100, previously described herein. Processor250 interprets instructions to execute software 240, as well as,generates automatic instructions to execute software for system 200responsive to predetermined conditions. Instructions from both userinterface 210 and software 240 are processed by processor 250 foroperation of system 200. In some embodiments, a plurality of processorscan be utilized such that system operations can be executed morerapidly.

Examples of modules for software 240 include, but are not limited to,Data Extraction Module 241, Data Preparation Module 243, Data MiningModule 245, and Alerts Module 247. Data Extraction Module 241 isconfigured to retrieve artificial lift data from database 230, such asby using software connectors, and feed it to Data Preparation Module243. Data Preparation Module 243 is configured to apply noise reductiontechniques and fault techniques to the extracted data, as well as, fillin missing data. Data Mining Module 247 is configured to extractfeatures from the processed data. Data Mining Module 247 is furtherconfigured to apply learning algorithms, such as supervised learningtechniques, to train, test and evaluate the results in the data miningstage, thereby detecting failures for the artificial lift systems.Alerts Module 247 provide alerts that an artificial lift system isdetected to fail.

In certain embodiments, system 200 can include reporting unit 260 toprovide information to the operator or to other systems (not shown). Forexample, reporting unit 260 can receive alerts from Alerts Module 247and output them to an operator or technician that an artificial liftsystem is detected to fail. The alert can be utilized to minimizedowntime of the artificial lift system or for other reservoir managementdecisions. Reporting unit 260 can be a printer, display screen, or adata storage device. However, it should be understood that system 200need not include reporting unit 260, and alternatively user interface210 can be utilized for reporting information of system 200 to theoperator.

Communication between any components of system 200, such as userinterface 210, well sensors 220, database 230, software 240, processor250 and reporting unit 260, can be transferred over a communicationsnetwork 270. Communications network 270 can be any means that allows forinformation transfer. Examples of communications network 270 presentlyinclude, but are not limited to, a switch within a computer, a personalarea network (PAN), a local area network (LAN), a wide area network(WAN), and a global area network (GAN). Communications network 270 canalso include any hardware technology used to connect the individualdevices in the network, such as an optical cable or wireless radiofrequency.

While in the foregoing specification this invention has been describedin relation to certain preferred embodiments thereof, and many detailshave been set forth for the purpose of illustration, it will be apparentto those skilled in the art that the invention is susceptible toalteration and that certain other details described herein can varyconsiderably without departing from the basic principles of theinvention.

Nomeclature AdaBNet: AdaBoost and Bayesian Network AdaDT: AdaBoost andDecision Tree SVM: Support Vector Machine PS1: Pre-Signal 1 PS2:Pre-Signal 2

Rec. Failure: Recorded Failure

POC: Pump Off Controller GB: Gerar Box HP: Horse Power DH: Down Hole

h_(t)(x): Weak or basis classifierα_(t): Weight of classifierH(x): Output classifierL_(exp)(x, y): Exponential loss functionL₀₋₁(x, y): 0-1 loss functionε_(t): Minimum fraction of errorsD_(t)(i): Distribution of weights over the training setP_(T)(error): Training errorKL(a∥b): Kullback-Leibler divergenceE: Objective function

We claim:
 1. A method of monitoring an artificial lift system in ahydrocarbon reservoir, comprising: monitoring a plurality of attributesof the artificial lift system to collect a data set characterizingperformance of the artificial lift system; applying a supervisedlearning algorithm to the collected data set to identify data valuesindicating a fault in the artificial lift system; and automaticallygenerating an alert in response to identification of a data valueindicating a fault in the artificial lift system.
 2. A method as inclaim 1, further comprising, pre-processing the data prior to theapplying the supervised learning algorithm.
 3. A method as in claim 2,wherein the pre-processing comprises applying a noise-reducing algorithmto the data.
 4. A method as in claim 1, wherein the applying thesupervised learning algorithm comprises
 5. A method as in claim 1,wherein the monitoring comprises measuring attributes collected frompump off controllers of an array of sucker rod pumps located in thehydrocarbon reservoir.
 6. A method as in claim 1, further comprising,partitioning the data into groups, the groups being ranked in accordancewith a metric based on relevancy to failure prediction.
 7. A method asin claim 6, wherein the metric is further based on data quality.
 8. Amethod as in claim 1, wherein the applying a supervised learningalgorithm comprises: processing training examples associated withpredetermined class labels to generate a failure detection model; andapplying the failure detection model to the collected data set.
 9. Amethod as in claim 1, wherein, when an alert is generated, a lookforward-backward process is executed as a verification of the alertcondition.
 10. A system for of monitoring an artificial lift system in ahydrocarbon reservoir, comprising: a processor configured and arrangedto receive a machine readable data set of attributes characterizingperformance of the artificial lift system; the processor beingprogrammed with machine executable instructions for applying asupervised learning algorithm to the collected data set to identify datavalues indicating a fault in the artificial lift system; and theprocessor being further programmed with machine executable instructionsfor automatically generating an alert in response to identification of adata value indicating a fault in the artificial lift system.
 11. Asystem as in claim 10, further comprising a plurality of detectors,configured and arranged to monitor the attributes of the artificial liftsystem, and in communication with a database for collecting themonitored attributes, the database being further in communication withthe processor.
 12. A tangible machine readable medium comprisinginstructions for executing machine executable instructions forperforming a method comprising: monitoring a plurality of attributes ofthe artificial lift system to collect a data set characterizingperformance of the artificial lift system; applying a supervisedlearning algorithm to the collected data set to identify data valuesindicating a fault in the artificial lift system; and automaticallygenerating an alert in response to identification of a data valueindicating a fault in the artificial lift system.